Data Science & Machine Learning – Telegram
Data Science & Machine Learning
72.1K subscribers
768 photos
1 video
68 files
677 links
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free

For collaborations: @love_data
Download Telegram
You will prefer YouTube videos in which language?
Anonymous Poll
15%
Hindi
66%
English
18%
Mix of both
Image recognition is an example of?
Anonymous Quiz
61%
Supervised Learning
39%
Unsupervised Learning
Data Science & Machine Learning
Image recognition is an example of?
You should know the difference between image classification and image recognition to answer this. Many people answered wrong maybe because of this confusion
👍2
Which of the following is an example of classification?
Anonymous Quiz
18%
Text Mining
32%
Face recognition
8%
Score prediction
42%
Fraud detection
Hello guys, so today I will share 5 amazing websites to learn data science and machine learning for free with Certificate. Do you want the complete video on how to enroll in those free certified courses?
Anonymous Poll
92%
Yes
8%
No
👍21
Top 5 websites to learn data science and machine learning absolutely Free
You will also get free Certificate after completing these courses
👇👇

https://youtu.be/kpJQTPO638M


Website links are given in the denoscription
Like and share this video with your friends who want to learn data science
Also, subscribe the channel for more content like this
👍101🥰1😁1
👍82
Top 10 Computer Vision Project Ideas

1. Edge Detection
2. Photo Sketching
3. Detecting Contours
4. Collage Mosaic Generator
5. Barcode and QR Code Scanner
6. Face Detection
7. Blur the Face
8. Image Segmentation
9. Human Counting with OpenCV
10. Colour Detection
👍41
Top Bayesian Algorithms and Methods:

- Naive Bayes.
- Averages one-dependence estimators.
- Bayesian belief networks.
- Gaussian naive Bayes.
- Multinomial naive Bayes.
- Bayesian networks.
👍5
An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.

Basically, there are 3 different layers in a neural network :

Input Layer (All the inputs are fed in the model through this layer)

Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)

Output Layer (The data after processing is made available at the output layer)

Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.
👍21
Which of the following is not a function of scikit learn?
Anonymous Poll
8%
classification
11%
regression  
12%
clustering
68%
data cleaning
👍1
Which Scipy package can be used for standard continuous and discrete probability distributions?
Anonymous Quiz
15%
Scipy.integrate
68%
Scipy.stats
8%
Scipy.signal
10%
Scipy.optimize